US20050149325A1 - Method of noise reduction using correction and scaling vectors with partitioning of the acoustic space in the domain of noisy speech - Google Patents

Abstract

A method and apparatus are provided for reducing noise in a training signal and/or test signal. The noise reduction technique uses a stereo signal formed of two channel signals, each channel containing the same pattern signal. One of the channel signals is “clean” and the other includes additive noise. Using feature vectors from these channel signals, a collection of noise correction and scaling vectors is determined. When a feature vector of a noisy pattern signal is later received, it is multiplied by the best scaling vector for that feature vector and the best correction vector is added to the product to produce a noise reduced feature vector. Under one embodiment, the best scaling and correction vectors are identified by choosing an optimal mixture component for the noisy feature vector. The optimal mixture component being selected based on a distribution of noisy channel feature vectors associated with each mixture component.

Description

REFERENCE TO RELATED APPLICATIONS
• This application is a divisional of and claims priority from U.S. patent application Ser. No. 09/688,764, filed Oct. 16, 2000 and entitled “METHOD OF NOISE REDUCTION USING CORRECTION AND SCALING VECTORS WITH PARTITIONING OF THE ACOUSTIC SPACE IN THE DOMAIN OF NOISY SPEECH.”
BACKGROUND OF THE INVENTION
• The present invention relates to noise reduction. In particular, the present invention relates to removing noise from signals used in pattern recognition.
• A pattern recognition system, such as a speech recognition system, takes an input signal and attempts to decode the signal to find a pattern represented by the signal. For example, in a speech recognition system, a speech signal (often referred to as a test signal) is received by the recognition system and is decoded to identify a string of words represented by the speech signal.
• To decode the incoming test signal, most recognition systems utilize one or more models that describe the likelihood that a portion of the test signal represents a particular pattern. Examples of such models include Neural Nets, Dynamic Time Warping, segment models, and Hidden Markov Models.
• Before a model can be used to decode an incoming signal, it must be trained. This is typically done by measuring input training signals generated from a known training pattern. For example, in speech recognition, a collection of speech signals is generated by speakers reading from a known text. These speech signals are then used to train the models.
• In order for the models to work optimally, the signals used to train the model should be similar to the eventual test signals that are decoded. In particular, the training signals should have the same amount and type of noise as the test signals that are decoded.
• Typically, the training signal is collected under “clean” conditions and is considered to be relatively noise free. To achieve this same low level of noise in the test signal, many prior art systems apply noise reduction techniques to the testing data. In particular, many prior art speech recognition systems use a noise reduction technique known as spectral subtraction.
• In spectral subtraction, noise samples are collected from the speech signal during pauses in the speech. The spectral content of these samples is then subtracted from the spectral representation of the speech signal. The difference in the spectral values represents the noise-reduced speech signal.
• Because spectral subtraction estimates the noise from samples taken during a limited part of the speech signal, it does not completely remove the noise if the noise is changing over time. For example, spectral subtraction is unable to remove sudden bursts of noise such as a door shutting or a car driving past the speaker.
• In another technique for removing noise, the prior art identifies a set of correction vectors from a stereo signal formed of two channel signals, each channel containing the same pattern signal. One of the channel signals is “clean” and the other includes additive noise. Using feature vectors that represent frames of these channel signals, a collection of noise correction vectors are determined by subtracting feature vectors of the noisy channel signal from feature vectors of the clean channel signal. When a feature vector of a noisy pattern signal, either a training signal or a test signal, is later received, a suitable correction vector is added to the feature vector to produce a noise reduced feature vector.
• Under the prior art, each correction vector is associated with a mixture component. To form the mixture component, the prior art divides the feature vector space defined by the clean channel's feature vectors into a number of different mixture components. When a feature vector for a noisy pattern signal is later received, it is compared to the distribution of clean channel feature vectors in each mixture component to identify a mixture component that best suits the feature vector. However, because the clean channel feature vectors do not include noise, the shapes of the distributions generated under the prior art are not ideal for finding a mixture component that best suits a feature vector from a noisy pattern signal.
• In addition, the correction vectors of the prior art only provided an additive element for removing noise from a pattern signal. As such, these prior art systems are less than ideal at removing noise that is scaled to the noisy pattern signal itself.
• In light of this, a noise reduction technique is needed that is more effective at removing noise from pattern signals.
SUMMARY OF THE INVENTION
• A method and apparatus are provided for reducing noise in a training signal and/or test signal used in a pattern recognition system. The noise reduction technique uses a stereo signal formed of two channel signals, each channel containing the same pattern signal. One of the channel signals is “clean” and the other includes additive noise. Using feature vectors from these channel signals, a collection of noise correction and scaling vectors is determined. When a feature vector of a noisy pattern signal is later received, it is multiplied by the best scaling vector for that feature vector and the product is added to the best correction vector to produce a noise reduced feature vector. Under one embodiment, the best scaling and correction vectors are identified by choosing an optimal mixture component for the noisy feature vector. The optimal mixture component being selected based on a distribution of noisy channel feature vectors associated with each mixture component.
BRIEF DESCRIPTION OF THE DRAWINGS
• FIG. 1 is a block diagram of one computing environment in which the present invention may be practiced.
• FIG. 2 is a block diagram of an alternative computing environment in which the present invention may be practiced.
• FIG. 3 is a flow diagram of a method of training a noise reduction system of the present invention.
• FIG. 4 is a block diagram of components used in one embodiment of the present invention to train a noise reduction system.
• FIG. 5 is a flow diagram of one embodiment of a method of using a noise reduction system of the present invention.
• FIG. 6 is a block diagram of a pattern recognition system in which the present invention may be used.
DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
• FIG. 1 illustrates an example of a suitable computing system environment100 on which the invention may be implemented. The computing system environment100 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment100 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment100.
• The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
• The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
• With reference toFIG. 1, an exemplary system for implementing the invention includes a general purpose computing device in the form of acomputer110. Components ofcomputer110 may include, but are not limited to, aprocessing unit120, asystem memory130, and asystem bus121 that couples various system components including the system memory to theprocessing unit120. Thesystem bus121 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus.
• Computer110 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed bycomputer110 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer100. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, FR, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media.
• Thesystem memory130 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)131 and random access memory (RAM)132. A basic input/output system133 (BIOS), containing the basic routines that help to transfer information between elements withincomputer110, such as during start-up, is typically stored inROM131.RAM132 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processingunit120. By way o example, and not limitation,FIG. 1 illustratesoperating system134,application programs135,other program modules136, andprogram data137.
• Thecomputer110 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,FIG. 1 illustrates ahard disk drive141 that reads from or writes to non-removable, nonvolatile magnetic media, amagnetic disk drive151 that reads from or writes to a removable, nonvolatilemagnetic disk152, and anoptical disk drive155 that reads from or writes to a removable, nonvolatileoptical disk156 such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. Thehard disk drive141 is typically connected to thesystem bus121 through a non-removable memory interface such asinterface140, andmagnetic disk drive151 andoptical disk drive155 are typically connected to thesystem bus121 by a removable memory interface, such asinterface150.
• The drives and their associated computer storage media discussed above and illustrated inFIG. 1, provide storage of computer readable instructions, data structures, program modules and other data for thecomputer110. InFIG. 1, for example,hard disk drive141 is illustrated as storingoperating system144,application programs145,other program modules146, andprogram data147. Note that these components can either be the same as or different fromoperating system134,application programs135,other program modules136, andprogram data137.Operating system144,application programs145,other program modules146, andprogram data147 are given different numbers here to illustrate that, at a minimum, they are different copies.
• A user may enter commands and information into thecomputer110 through input devices such as akeyboard162, amicrophone163, and apointing device161, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to theprocessing unit120 through auser input interface160 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). Amonitor191 or other type of display device is also connected to thesystem bus121 via an interface, such as avideo interface190. In addition to the monitor, computers may also include other peripheral output devices such asspeakers197 andprinter196, which may be connected through an outputperipheral interface190.
• Thecomputer110 may operate in a networked environment using logical connections to one or more remote computers, such as aremote computer180. Theremote computer180 may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to thecomputer110. The logical connections depicted inFIG. 1 include a local area network (LAN)171 and a wide area network (WAN)173, but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.
• When used in a LAN networking environment, thecomputer110 is connected to theLAN171 through a network interface oradapter170. When used in a WAN networking environment, thecomputer110 typically includes amodem172 or other means for establishing communications over theWAN173, such as the Internet. Themodem172, which may be internal or external, may be connected to thesystem bus121 via theuser input interface160, or other appropriate mechanism. In a networked environment, program modules depicted relative to thecomputer110, or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,FIG. 1 illustratesremote application programs185 as residing onremote computer180. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.
• FIG. 2 is a block diagram of amobile device200, which is an exemplary computing environment.Mobile device200 includes amicroprocessor202,memory204, input/output (I/O)components206, and acommunication interface208 for communicating with remote computers or other mobile devices. In one embodiment, the afore-mentioned components are coupled for communication with one another over asuitable bus210.
• Memory204 is implemented as non-volatile electronic memory such as random access memory (RAM) with a battery back-up module (not shown) such that information stored inmemory204 is not lost when the general power tomobile device200 is shut down. A portion ofmemory204 is preferably allocated as addressable memory for program execution, while another portion ofmemory204 is preferably used for storage, such as to simulate storage on a disk drive.
• Memory204 includes anoperating system212,application programs214 as well as anobject store216. During operation,operating system212 is preferably executed byprocessor202 frommemory204.Operating system212, in one preferred embodiment, is a WINDOWS® CE brand operating system commercially available from Microsoft Corporation.Operating system212 is preferably designed for mobile devices, and implements database features that can be utilized byapplications214 through a set of exposed application programming interfaces and methods. The objects inobject store216 are maintained byapplications214 andoperating system212, at least partially in response to calls to the exposed application programming interfaces and methods.
• Communication interface208 represents numerous devices and technologies that allowmobile device200 to send and receive information. The devices include wired and wireless modems, satellite receivers and broadcast tuners to name a few.Mobile device200 can also be directly connected to a computer to exchange data therewith. In such cases,communication interface208 can be an infrared transceiver or a serial or parallel communication connection, all of which are capable of transmitting streaming information.
• Input/output components206 include a variety of input devices such as a touch-sensitive screen, buttons, rollers, and a microphone as well as a variety of output devices including an audio generator, a vibrating device, and a display. The devices listed above are by way of example and need not all be present onmobile device200. In addition, other input/output devices may be attached to or found withmobile device200 within the scope of the present invention.
• Under the present invention, a system and method are provided that reduce noise in pattern recognition signals. To do this, the present invention identifies a collection of scaling vectors, S_k, and correction vectors, r_k, that can be respectively multiplied by and added to a feature vector representing a portion of a noisy pattern signal to produce a feature vector representing a portion of a “clean” pattern signal. A method for identifying the collection of scaling vectors and correction vectors is described below with reference to the flow diagram ofFIG. 3 and the block diagram ofFIG. 4. A method of applying scaling vectors and correction vectors to noisy feature vectors is described below with reference to the flow diagram ofFIG. 5 and the block diagram ofFIG. 6.
• The method of identifying scaling vectors and correction vectors begins instep300 ofFIG. 3, where a “clean” channel signal is converted into a sequence of feature vectors. To do this, aspeaker400 ofFIG. 4, speaks into amicrophone402, which converts the audio waves into electrical signals. The electrical signals are then sampled by an analog-to-digital converter404 to generate a sequence of digital values, which are grouped into frames of values by aframe constructor406. In one embodiment, A-to-D converter404 samples the analog signal at 16 kHz and 16 bits per sample, thereby creating 32 kilobytes of speech data per second andframe constructor406 creates a new frame every 10 milliseconds that includes 25 milliseconds worth of data.
• Each frame of data provided byframe constructor406 is converted into a feature vector by afeature extractor408. Examples of feature extraction modules include modules for performing Linear Predictive Coding (LPC), LPC derived cepstrum, Perceptive Linear Prediction (PLP), Auditory model feature extraction, and Mel-Frequency Cepstrum Coefficients (MFCC) feature extraction. Note that the invention is not limited to these feature extraction modules and that other modules may be used within the context of the present invention.
• Instep302 ofFIG. 3, a noisy channel signal is converted into feature vectors. Although the conversion ofstep302 is shown as occurring after the conversion ofstep300, any part of the conversion may be performed before, during or afterstep300 under the present invention. The conversion ofstep302 is performed through a process similar to that described above forstep300.
• In the embodiment ofFIG. 4, this process begins when the same speech signal generated byspeaker400 is provided to asecond microphone410. This second microphone also receives an additive noise signal from anadditive noise source412.Microphone410 converts the speech and noise signals into a single electrical signal, which is sampled by an analog-to-digital converter414. The sampling characteristics for A/D converter414 are the same as those described above for A/D converter404. The samples provided by A/D converter414 are collected into frames by aframe constructor416, which acts in a manner similar toframe constructor406. These frames of samples are then converted into feature vectors by afeature extractor418, which uses the same feature extraction method asfeature extractor408.
• In other embodiments,microphone410, A/D converter414,frame constructor416 andfeature extractor418 are not present. Instead, the additive noise is added to a stored version of the speech signal at some point within the processing chain formed bymicrophone402, A/D converter404,frame constructor406, andfeature extractor408. For example, the analog version of the “clean” channel signal may be stored after it is created bymicrophone402. The original “clean” channel signal is then applied to A/D converter404,frame constructor406, andfeature extractor408. When that process is complete, an analog noise signal is added to the stored “clean” channel signal to form a noisy analog channel signal. This noisy signal is then applied to A/D converter404,frame constructor406, andfeature extractor408 to form the feature vectors for the noisy channel signal.
• In other embodiments, digital samples of noise are added to stored digital samples of the “clean” channel signal between A/D converter404 andframe constructor406, or frames of digital noise samples are added to stored frames of “clean” channel samples afterframe constructor406. In still further embodiments, the frames of “clean” channel samples are converted into the frequency domain and the spectral content of additive noise is added to the frequency-domain representation of the “clean” channel signal. This produces a frequency-domain representation of a noisy channel signal that can be used for feature extraction.
• The feature vectors for the noisy channel signal and the “clean” channel signal are provided to anoise reduction trainer420 inFIG. 4. Atstep304 ofFIG. 3,noise reduction trainer420 groups the feature vectors for the noisy channel signal into mixture components. This grouping can be done by grouping feature vectors of similar noises together using a maximum likelihood training technique or by grouping feature vectors that represent a temporal section of the speech signal together. Those skilled in the art will recognize that other techniques for grouping the feature vectors may be used and that the two techniques listed above are only provided as examples.
• After the feature vectors of the noisy channel signal have been grouped into mixture components,noise reduction trainer420 generates a set of distribution values that are indicative of the distribution of the feature vectors within the mixture component. This is shown asstep306 inFIG. 3. In many embodiments, this involves determining a mean vector and a standard deviation vector for each vector component in the feature vectors of each mixture component. In an embodiment in which maximum likelihood training is used to group the feature vectors, the means and standard deviations are provided as by-products of identifying the groups for the mixture components.
• Once the means and standard deviations have been determined for each mixture component, thenoise reduction trainer420 determines a correction vector, r_k, and a scaling vector Sk, for each mixture component, k, atstep308 ofFIG. 3. Under one embodiment, the vector components of the scaling vector and the vector components of the correction vector for each mixture component are determined using a weighted least squares estimation technique. Under this technique, the scaling vector components are calculated as:$\begin{array}{cc}{S}_{i,k}=\frac{\begin{array}{c}\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)y_{i,t}\right]\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)x_{i,t}\right]-\\ \left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)\right]\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)x_{i,t}{y}_{i,t}\right]\end{array}}{\begin{array}{c}{\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)y_{i,t}\right]}^2-\\ \left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)\right]\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)y_{i,t}^2\right]\end{array}}& \mathrm{EQ}.1\end{array}$
• sand the correction vector components are calculated as:$\begin{array}{cc}{r}_{i,k}=\frac{\begin{array}{c}\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)y_{i,t}\right]\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)x_{i,t}{y}_{i,t}\right]-\\ \left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)x_{i,t}\right]\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)y_{i,t}^2\right]\end{array}}{\begin{array}{c}{\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)y_{i,t}\right]}^2-\\ \left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)\right]\left[\sum _{t=0}^{T-1}p\left(k|y_{i,t}\right)y_{i,t}^2\right]\end{array}}& \mathrm{EQ}.2\end{array}$
• Where S_i,kis the i^thvector component of a scaling vector, S_k, for mixture component k , r_i,kis the i^thvector component of a correction vector, r_k, for mixture component k, y_i,tis the i^thvector component for the feature vector in the t^thframe of the noisy channel signal, x_i,tis the i^thvector component for the feature vector in the t^thframe of the “clean” channel signal, T is the total number of frames in the “clean” and noisy channel signals, and p(k|y_i,t) is the probability of the k^thmixture component given the feature vector component for the t^thframe of the noisy channel signal.
• In equations 1 and 2, the p(k|y_i,t) term provides a weighting function that indicates the relative relationship between the k^thmixture component and the current frame of the channel signals.
• The p(k|y_i,t) term can be calculated using Bayes' theorem as:$\begin{array}{cc}p\left(k|y_{i,t}\right)=\frac{p\left(y_{i,t}|k\right)p\left(k\right)}{\sum _{\mathrm{all}k}p\left(y_{i,t}|k\right)p\left(k\right)}& \mathrm{EQ}.3\end{array}$
• Where p(y_i,t|k) is the probability of the i^thvector component in the noisy feature vector given the k^thmixture component, and p(k) is the probability of the k^thmixture component.
• The probability of the i^thvector component in the noisy feature vector given the k^thmixture component, p(y_i,t|k), can be determined using a normal distribution based on the distribution values determined for the k^thmixture component instep306 ofFIG. 3. In one embodiment, the probability of the k^thmixture component, p(k), is simply the inverse of the number of mixture components. For example, in an embodiment that has 256 mixture components, the probability of any one mixture component is 1/256.
• After a correction vector and a scaling vector have been determined for each mixture component atstep308, the process of training the noise reduction system of the present invention is complete. The correction vectors, scaling vectors, and distribution values for each mixture component are then stored in a noisereduction parameter storage422 ofFIG. 4.
• Once the correction vector and scaling vector have been determined for each mixture, the vectors may be used in a noise reduction technique of the present invention. In particular, the correction vectors and scaling vectors may be used to remove noise in a training signal and/or test signal used in pattern recognition.
• FIG. 5 provides a flow diagram that describes the technique for reducing noise in a training signal and/or test signal. The process ofFIG. 5 begins atstep500 where a noisy training signal or test signal is converted into a series of feature vectors. The noise reduction technique then determines which mixture component best matches each noisy feature vector. This is done by applying the noisy feature vector to a distribution of noisy channel feature vectors associated with each mixture component. In one embodiment, this distribution is a collection of normal distributions defined by the mixture component's mean and standard deviation vectors. The mixture component that provides the highest probability for the noisy feature vector is then selected as the best match for the feature vector. This selection is represented in an equation as:
  {circumflex over (k)}=arg_kmaxc_kN(y; μ_k,Σ_k)   EQ. 4
• Where {circumflex over (k)} is the best matching mixture component, c_kis a weight factor for the k^thmixture component, N(y;μ_k,Σ_k) is the value for the individual noisy feature vector, y, from the normal distribution generated for the mean vector, μ_k, and the standard deviation vector, Σ_k, of the k^thmixture component. In most embodiments, each mixture component is given an equal weight factor c_k.
• Note that under the present invention, the mean vector and standard deviation vector for each mixture component is determined from noisy channel vectors and not “clean” channel vectors as was done in the prior art. Because of this, the normal distributions based on these means and standard deviations are better shaped for finding a best mixture component for a noisy pattern vector.
• Once the best mixture component for each input feature vector has been identified atstep502, the corresponding scaling and correction vectors for those mixture components are (element by element) multiplied by and added to the individual feature vectors to form “clean” feature vectors. In terms of an equation:
  x_i=S_i,ky_i+r_i,k  EQ. 5
• Where x_iis the i^thvector component of an individual “clean” feature vector, y_iis the i^thvector component of an individual noisy feature vector from the input signal, and S_i,kand r_i,kare the i^thvector component of the scaling and correction vectors, respectively, both optimally selected for the individual noisy feature vector. The operation of Equation 5 is repeated for each vector component. Thus, Equation 5 can be re-written in vector notation as:
  x=S_ky+r_k  EQ. 5
• • where x is the “clean” feature vector, S_kis the scaling vector, y is the noisy feature vector, and r_kis the correction vector.
• FIG. 6 provides a block diagram of an environment in which the noise reduction technique of the present invention may be utilized. In particular,FIG. 6 shows a speech recognition system in which the noise reduction technique of the present invention is used to reduce noise in a training signal used to train an acoustic model and/or to reduce noise in a test signal that is applied against an acoustic model to identify the linguistic content of the test signal.
• InFIG. 6, aspeaker600, either a trainer or a user, speaks into amicrophone604.Microphone604 also receives additive noise from one or more noise sources602. The audio signals detected bymicrophone604 are converted into electrical signals that are provided to analog-to-digital converter606. Althoughadditive noise602 is shown entering throughmicrophone604 in the embodiment ofFIG. 6, in other embodiments,additive noise602 may be added to the input speech signal as a digital signal after A-to-D converter606.
• A-to-D converter606 converts the analog signal frommicrophone604 into a series of digital values. In several embodiments, A-to-D converter606 samples the analog signal at 16 kHz and 16 bits per sample, thereby creating 32 kilobytes of speech data per second. These digital values are provided to aframe constructor607, which, in one embodiment, groups the values into 25 millisecond frames that start 10 milliseconds apart.
• The frames of data created byframe constructor607 are provided to featureextractor610, which extracts a feature from each frame. The same feature extraction that was used to train the noise reduction parameters (the scaling vectors, correction vectors, means, and standard deviations of the mixture components) is used infeature extractor610. As mentioned above, examples of such feature extraction modules include modules for performing Linear Predictive Coding (LPC), LPC derived cepstrum, Perceptive Linear Prediction (PLP), Auditory model feature extraction, and Mel-Frequency Cepstrum Coefficients (MFCC) feature extraction.
• The feature extraction module produces a stream of feature vectors that are each associated with a frame of the speech signal. This stream of feature vectors is provided tonoise reduction module610 of the present invention, which uses the noise reduction parameters stored in noisereduction parameter storage611 to reduce the noise in the input speech signal. In particular, as shown inFIG. 5,noise reduction module610 selects a single mixture component for each input feature vector and then multiplies the input feature vector by that mixture component's scaling vector and adding that mixture component's correction vector to the product to produce a “clean” feature vector.
• Thus, the output ofnoise reduction module610 is a series of “clean” feature vectors. If the input signal is a training signal, this series of “clean” feature vectors is provided to atrainer624, which uses the “clean” feature vectors and atraining text626 to train anacoustic model618. Techniques for training such models are known in the art and a description of them is not required for an understanding of the present invention.
• If the input signal is a test signal, the “clean” feature vectors are provided to adecoder612, which identifies a most likely sequence of words based on the stream of feature vectors, alexicon614, alanguage model616, and theacoustic model618. The particular method used for decoding is not important to the present invention and any of several known methods for decoding may be used.
• The most probable sequence of hypothesis words is provided to aconfidence measure module620.Confidence measure module620 identifies which words are most likely to have been improperly identified by the speech recognizer, based in part on a secondary acoustic model(not shown).Confidence measure module620 then provides the sequence of hypothesis words to anoutput module622 along with identifiers indicating which words may have been improperly identified. Those skilled in the art will recognize thatconfidence measure module620 is not necessary for the practice of the present invention.
• AlthoughFIG. 6 depicts a speech recognition system, the present invention may be used in any pattern recognition system and is not limited to speech.
• Although the present invention has been described with reference to particular embodiments, workers skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the invention.

Claims (16)

1. A method of noise reduction for reducing noise in a noisy input signal, the method comprising:

grouping noisy channel feature vectors and clean channel feature vectors into a plurality of mixture components;

fitting a function applied to noisy channel feature vectors associated with a mixture component to only those clean channel feature vectors that are associated with the same mixture component to determine at least one correction vector and at least one scaling vector;

multiplying the scaling vector by a noisy input feature vector to produce a scaled feature vector; and

adding a correction vector to the scaled feature vector to form a clean input feature vector.

2. The method ofclaim 1 wherein determining a correction vector comprises:

grouping the noisy channel feature vectors into at least one mixture component;

determining a distribution value that is indicative of the distribution of the noisy channel feature vectors in at least one mixture component; and

using the distribution value for a mixture component to determine the correction vector and the scaling vector for that mixture component.

3. The method ofclaim 2 wherein using the distribution value to determine a correction vector and a scaling vector for a mixture component comprises:

determining, for each noisy channel feature vector, at least one conditional mixture probability, the conditional mixture probability representing the probability of the mixture component given the noisy channel feature vector, the conditional mixture probability based in part on a distribution value for the mixture component; and

applying the conditional mixture probability in a linear least squares calculation.

4. The method ofclaim 3 wherein determining a conditional mixture probability comprises:

determining a conditional feature vector probability that represents the probability of a noisy channel feature vector given the mixture component, the probability based on the distribution value for the mixture;

multiplying the conditional feature vector probability by the unconditional probability of the mixture component to produce a probability product; and

dividing the probability product by the sum of the probability products generated for all mixture components for the noisy channel feature vector.

5. The method ofclaim 4 wherein determining a conditional feature vector probability comprises determining the probability from a normal distribution formed from the distribution value for a mixture component.

6. The method ofclaim 5 wherein determining a distribution value comprises determining a mean vector and determining a standard deviation vector.

7. The method ofclaim 1 wherein multiplying the scaling vector by a noisy input feature vector comprises:

identifying a mixture component for the noisy input feature vector; and

multiplying the noisy input feature vector by a scaling vector associated with the mixture component.

8. The method ofclaim 7 wherein adding a correction vector comprises adding a correction vector associated with the mixture component to the scaled feature vector.

9. The method ofclaim 8 wherein identifying a mixture component comprises identifying the most likely mixture component for the noisy input feature vector.

10. The method ofclaim 9 wherein identifying the most likely mixture component comprises:

grouping the noisy channel feature vectors into at least one mixture component;

determining a distribution value that is indicative of the distribution of the noisy channel feature vectors in at least one mixture component;

for each mixture component, determining a probability of the noisy input feature vector given the mixture component based on a normal distribution formed from the distribution value for that mixture component; and

selecting the mixture component that provides the highest probability as the most likely mixture component.

11. A method of reducing noise in a noisy signal, the method comprising:

identifying a single mixture component for a noisy feature vector representing a part of the noisy signal;

retrieving a correction vector and a scaling vector associated with the identified mixture component;

multiplying the noisy feature vector by the scaling vector to form a scaled feature vector; and

adding the correction vector to the scaled feature vector to form a clean feature vector representing a part of a clean signal.

12. The method ofclaim 11 wherein identifying a mixture component comprises identifying a most likely mixture component for a noisy feature vector.

13. A method of generating correction values for removing noise from an input signal, the method comprising:

accessing a set of noisy channel vectors representing a noisy channel signal;

accessing a set of clean channel vectors representing a clean channel signal;

grouping the noisy channel vectors into a plurality of mixture components based on the noisy channel vectors; and

determining a correction value for a mixture component.

14. The method ofclaim 13 wherein determining a correction value comprises fitting a function based on noisy channel vectors to clean channel vectors.

15. The method ofclaim 14 wherein fitting a function comprises performing a linear least squares calculation.

16. The method ofclaim 13 wherein determining a correction value comprises determining an additive correction value and a scaling correction value.

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